Los Cabos Open stats & predictions

Anteprima del Torneo Tennis Los Cabos Open in Messico

Benvenuti alla guida dettagliata sul Tennis Los Cabos Open, uno degli eventi più attesi del circuito professionistico. Questo prestigioso torneo fa parte dell'ATP Tour 250 e si svolge annualmente sulla spiaggia incantevole di Los Cabos, in Messico. Con i campi in terra battuta come scenario, gli appassionati di tennis possono aspettarsi un'emozione indimenticabile e partite avvincenti.

Cosa Aspettarsi Domani?

Nella giornata di domani, i campi vedranno alcune delle migliori talenti del tennis pronti a confrontarsi in partite che promettono spettacolo. Ecco un anticipo di ciò che ci riserva la giornata che sta per iniziare:

• Match Principali: Tra gli incontri più attesi c’è quello tra la stella locale e il campione mondiale, una sfida che promette colpi mozzafiato e strategie avvincenti.
• Emergenti: I giovani talenti internazionali non si fanno sfuggire l’occasione di fare la differenza, e molti di loro sono pronti a stupire con prestazioni fuori dal comune.
• La Partecipazione delle Giustizie: Anche le giocatrici hanno nutriti match in programma, pronte a lottare per un posto nei quarti di finale.

Le Proiezioni sui Pronostici

Per gli amanti dei pronostici e dei betting, ecco alcuni suggerimenti basati su analisi dettagliate delle partite di domani. Si tratta di previsioni affidabili che tengono conto delle performance recenti dei giocatori e delle dinamiche in campo:

1. Nome del Giocatore 1: Con una forma straordinaria ed un ranking in ascesa, è probabile che questa partita metta in mostra tutte le sue abilità strategiche.
2. Nome del Giocatore 2: Un predittore molto sicuro è la vittoria di questo atleta, che ha dimostrato una grande resilienza nei match recenti e potrebbe ribaltare la situazione a suo favore.
3. Il Potenziale di Sorprese: Non sottovalutate le underdog della giornata; a volte, una performance impareggiabile può emergere dall’imprevisto, offrendo delle possibilità di profitto inaspettate.

Consigli Tecnici per le Scommesse

Ecco alcuni consigli essenziali per chi approccia il betting sportivo con spirito analitico e strategico:

• Analizza le Statistiche Recenti: Le partite passate possono offrire indicazioni cruciali sui cambiamenti nelle performance e nelle dinamiche di gioco.
• Considera le Condizioni del Campo: La terra battuta di Los Cabos può influenzare il gioco in modo significativo; i giocatori con una buona resistenza e strategia potrebbero avere un vantaggio.
• Valuta il Forma Fisica: Un giocatore in forma fisica ottimale ha maggiori possibilità di affrontare i match con una intensità sostenuta e potrebbe incrementare le possibilità di successo.

Guida Rapida ai Match delle Prossime Ora

Per coloro che sono intenzionati a seguire i match in diretta, ecco una rapida panoramica delle sfide previste nelle prossime ore:

Curiosità e Didascalie sul Campo

Oltre alla natura altamente competitiva del torneo, il Los Cabos Open offre ai visitatori un'esperienza culturale unica. Il paesaggio mozzafiato delle coste messicane serve da sfondo ideale alle emozionanti partite:

• Paesaggio e Atmosfera: La spiaggia di Playa Gaviota Sur offre una vista spettacolare non solo per i giocatori ma anche per il pubblico, creando un'atmosfera eccezionale.
• Supporto Loca<|repo_name|>G2-v2/Overshoot<|file_sep|>/CompVis-Demo.md # So you want to be a Computer Vision_DEV? ## Predicting the future of computer vision ### developed by Team OVERSHOOT ### written with Python 3.x | demoed with Python 2.x --- ### What is computer vision? Computer vision makes computers see the world like humans do. It takes pictures brought in by a camera or other image sensor and processes them to understand the contents of the picture or to generate useful information for further processing. It is a field of research and engineering that is closely related to imaging science, computer science and other fields of science that work together to "transform the visual world into numerical data that can be manipulated by computers effectively and efficiently." (Source: CS231n: Convolutional Neural Networks For Visual Recognition Assignment 1: Image Classification, Stanford University, Spring 2017.) ### What is a Convolutional Neural Network? Convolutional Neural Networks (CNN) are deep learning algorithms that take inspiration from the connectivity patterns of neurons in the human brain and have been applied to analyzing visual imagery. CNNs use adjacent pixels so they can assign significance to the spatial location of pixels based on its neighbors. This means they can learn spatial hierarchies of patterns.  ## Team OVERSHOOT's Operation - Download the Python file and install all dependencies below (tensorflow website updated at the time of writing) - Select Image one at a time from the root folder - A model will predict and show N classes (see below) with their percentage of match.   You can also open and read all images below in a for loop if wanted. ## Classifications Used - Traffic Signs Dataset that is collected from Gui Yaoyuneyong (UIUC), and Olaf Ronneberger (KIT) - We have used GoogleNet Inception V3 model which has over 21.8 Million weights but only provided 4 MB parameters. This allows GoogleNet to be both powerful yet lighter on computation. GoogleNet has about 57 layers and uses ReLu as an activation function. GoogleNet is known for using an architecture innovation called Inception rich feature hierarchy with the module known as inception module which contains 1x1 convolutions, 3x3 convolutions, and 5x5 convolutions acting as filters. The Inception Module can be seen below.

  

  It is claimed that this reduces the dimensionality of the feature space used by usual convolution layers. ## Hyperparameter Tuning ## Hypothesis Testing ## Check Check Checklist! #### Dependencies: - TensorFlow (with CUDA and CuDNN, if available on your machine, for GPU support) - Pillow-SIMD (or Pillow) - Apache Commons Math (jar file) - NumPy #### Installations: 1. At the start of "CompVis.py" comment out lines 25 and 26. 2. In command line run: python -m pip install -r requirements.txt 3. Run your file: python CompVis.py #### Contribute: Feel free to fork our repo and submit prs for improvements we might have not thought of! #### Warning: - The above process is not guaranteed to work on your setup. - This is work in Progress. - Data is generated by scraping or using mobile apps and stored publicly. <|repo_name|>G2-v2/Overshoot<|file_sep|>/requirements.txt glob2==0.6 numpy matplotlib tensorflow scipy sklearn pillow apache-commons-math numba <|repo_name|>G2-v2/Overshoot<|file_sep|>/CompVis.py # Basic imports for this system to run from glob import glob import numpy as np from datetime import datetime import time import matplotlib.pyplot as plt import os # These imports enable proper installation of the pip requirements.txt file from tensorflow.python.layers.core import Dense from tensorflow.python.ops import nn_ops # System Package Imports from PIL import Image as PILImage from sklearn.metrics import accuracy_score, confusion_matrix, classification_report from sklearn.model_selection import train_test_split # Machine Learning packages import tensorflow as tf import math # pip install apache-commons-math-1.2.jar import commons_math # Code cleanliness import tflearn as tl # Lazy Global Variables (AKA | Global Variables) # The class names depending on the order of the classes that GoogleNet trained for your model. CLASSES = ['Speed Limit (20km/h)', 'Speed Limit (30km/h)', 'Speed Limit (50km/h)', 'Speed Limit (60km/h)', 'Speed Limit (70km/h)', 'Speed Limit (80km/h)', 'End of Speed Limit (80km/h)', 'Speed Limit (100km/h)', 'Speed Limit (120km/h)', 'No Passing', 'No Passing For Vehicles Over 3.5 Metric Tons', 'Right-of-Way at Next Intersection', 'Priority Road', 'Yield', 'Stop', 'No Vehicles', 'Vehicles Over 3.5 Metric Tons Prohibited', 'No Entry', 'General Caution', 'Dangerous Curve to the Left', 'Dangerous Curve to the Right', 'Double Curve', 'Bumpy Road', 'Slippery Road', 'Road Narrow to the Right', 'Road Work', 'Traffic Signals', 'Pedestrians', 'Children Crossing', 'Bicycles Crossing', 'Beware of Ice/Snow', 'Wild Animals Crossing', 'End of All Speed and Passing Limits', 'Turn Right Ahead', 'Turn Left Ahead', 'Ahead Only', 'Go Straight or Right', 'Go Straight or Left', 'Keep Right', 'Keep Left', 'Roundabout Mandatory', 'End of No Passing', 'End of Speed Limit (80km/h)'] # Resnet Parameters LEARNING_RATE = 0.001 BATCH_SIZE = 32 # Number of images for reference EPOCHS = 16 RESNET_SIZE = [32] VERBOSE = True # True = Nobody will want to read the logs # CNN Parameters. USING_BASIC_MODEL = False MODEL_FILE = "model_inception_v3_2017_11_17" # Saved models to load if available :) INPUT_SIZE = [None, 32,32,3] # Data Arrays. INTERMEDIATE_ARRAY_SIZE = 10000 # For Array Intermediates With BATCH_SIZE = 32 | SMALL_INPUT_SIZE = 0 AVERAGE_COMPUTATION_TIME = [0, 1 / INTERMEDIATE_ARRAY_SIZE] # This is where we chose our input directory for images INPUT_DIRECTORY = "Data/All/" IMAGES_PATH = os.path.join(INPUT_DIRECTORY, "*.jpg") # Jpg Images for googlenet def load_images(paths_file): """ Creates a generator for iterating images one by one. """ # Get all the jpg images inside File Structure. files = glob(os.path.join(IMAGES_PATH)) np.random.shuffle(files) print("Total Files: {}".format(len(files))) for i_file, path in enumerate(files): image_data = PILImage.open(path) # If Image is invalid then raise error. if not image_data: print("File not found: ", path) raise IOError # Check if image is grayscale and issue warning if len(image_data.split()) != 3: print("WARNING: {} file is not RGB.".format(path)) continue # Skip this iteration and move to the next. image_data = np.asarray(image_data.resize(RESNET_SIZE)) label = [] if path.find("New") == -1: label_name = path.split(os.path.sep)[-2] # Check if label name is not in our list of classes. If so problem. if label_name not in CLASSES: print("{} not found in Classes.".format(label_name)) continue label = CLASSES.index(label_name) yield image_data, label def evaluate_model(inputs_X): """ :param inputs_X: The images after reading a pickle file. :return: The best result for accuracy score for full batch & single batch. """ results = [] # Prediction block. if len(RESNET_SIZE) == 1: with tf.name_scope("results"): prediction = tf.nn.softmax(vgg_model(inputs_X)) results.append(np.argmax(prediction.eval(), axis=1)) elif len(RESNET_SIZE) == 2: with tf.name_scope("results"): inputs = tf.reshape(inputs_X, [-1] + list(RESNET_SIZE[1]) + [list(RESNET_SIZE[0])]) prediction = tf.nn.softmax(vgg_model(inputs)) results.append(np.argmax(prediction.eval(), axis=1)) else: raise ValueError("Shape needs to be [2]: width, height.") return results if __name__ == '__main__': print("Program started at {}".format(datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S"))) # Prep Operations For Tensorflow Programming. start_time = time.time() # Open Tensorflow Session. with tf.Session() as sess: # Set up placeholder input variable for read image data. inputs_X = tf.placeholder(tf.float32, shape=INPUT_SIZE) # GoogleNet Input Size. # Create VGG16 Object from Object Pool For Declaration. vgg16_conv = VGG16(weights='imagenet', include_top=False) account_inputs = tflearn.input_data(shape=[None, None, None, vgg16_conv.layers[-1].output_shape[-1]]) # Now we create our Output Layer with Dropouts. conv2_dropout = tl.layers.Dropout(conv2_linear_7_64, keep=0.8)(conv2_linear_7_64) conv3_dropout = tl.layers.Dropout(conv3_linear_7_128, keep=0.8)(conv3_linear_7_128) pool3_dropout = tl.layers.Dropout(conv4_pool_7_128)(conv4_pool_7_128) # Create our dropouts with decay. conv2_dropout_decay = tf.nn.dropout(conv2_dropout.outputs, conv2_dropout.get_keep_prob()) conv3_dropout_decay = tf.nn.dropout(conv3_dropout.outputs, conv3_dropout.get_keep_prob()) pool3_dropout_decay = tf.nn.dropout(pool3_dropout.outputs, pool3_dropout.get_keep_prob()) # Calculate the average deviation from a list of floats. average_cuppled = np.mean([AVERAGE_COMPUTATION_TIME[0], AVERAGE_COMPUTATION_TIME[1]]) # Calculate how many iterations it takes to reach INPUT_SIZE[0]. total_times = math.ceil(INPUT_SIZE[0] / INTERMEDIATE_ARRAY_SIZE) average_times = BATCH_SIZE * total_times # Multiply by average coupled times to get better stabilized times. average_times *= average_cuppled # Training handling time per image (let's hope it's correct). training_per_second = INTERMEDIATE_ARRAY_SIZE / (time.time() - start_time) training_per_second *= average_cuppled # Now we calculate to hours and seconds. training_hours = math.floor(average_times / training_per_second / 3600) training_seconds = training_hours * 3600 / training_per_second print("Training should take: {} Hours Total {} Seconds".format(training_hours, training_seconds)) # Prepare Our Input Sizes for setting up like Convolution Channels [32,32,3] GoogleNet size. if len(RESNET_SIZE) == 1: inputs_reshaped = tf.reshape(inputs_X, [-1] + list(RESNET_SIZE[1]) + [list(RESNET_SIZE[0])]) elif len(RESNET_SIZE) == 2: inputs_reshaped = tf.reshape(inputs_X, [-1] + list(RESNET_SIZE[1]) + [list(RESNET_SIZE[0])]) else: raise